Package: BayesS5 1.41

BayesS5: Bayesian Variable Selection Using Simplified Shotgun Stochastic Search with Screening (S5)

In p >> n settings, full posterior sampling using existing Markov chain Monte Carlo (MCMC) algorithms is highly inefficient and often not feasible from a practical perspective. To overcome this problem, we propose a scalable stochastic search algorithm that is called the Simplified Shotgun Stochastic Search (S5) and aimed at rapidly explore interesting regions of model space and finding the maximum a posteriori(MAP) model. Also, the S5 provides an approximation of posterior probability of each model (including the marginal inclusion probabilities). This algorithm is a part of an article titled "Scalable Bayesian Variable Selection Using Nonlocal Prior Densities in Ultrahigh-dimensional Settings" (2018) by Minsuk Shin, Anirban Bhattacharya, and Valen E. Johnson and "Nonlocal Functional Priors for Nonparametric Hypothesis Testing and High-dimensional Model Selection" (2020+) by Minsuk Shin and Anirban Bhattacharya.

Authors:Minsuk Shin and Ruoxuan Tian

BayesS5_1.41.tar.gz
BayesS5_1.41.zip(r-4.5)BayesS5_1.41.zip(r-4.4)BayesS5_1.41.zip(r-4.3)
BayesS5_1.41.tgz(r-4.4-any)BayesS5_1.41.tgz(r-4.3-any)
BayesS5_1.41.tar.gz(r-4.5-noble)BayesS5_1.41.tar.gz(r-4.4-noble)
BayesS5_1.41.tgz(r-4.4-emscripten)BayesS5_1.41.tgz(r-4.3-emscripten)
BayesS5.pdf |BayesS5.html✨
BayesS5/json (API)

Peer review:

On CRAN:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

17 exports 2 stars 0.23 score 8 dependencies 10 scripts 240 downloads

Last updated 4 years agofrom:1d78d767f0. Checks:OK: 7. Indexed: yes.

Exports:Bernoulli_Uniformhyper_parind_fun_gind_fun_NLfPind_fun_pemomind_fun_pimomobj_fun_gobj_fun_pemomobj_fun_pimomresultresult_est_LSresult_est_MAPS5S5_additiveS5_parallelSSSUniform

Dependencies:abindlatticeMatrixRcppRcppArmadillosnowsnowfallsplines2